# QBFF: Deep Quantum Bottleneck for Automatic Speaker Verification Anti-Spoofing

arXiv: https://arxiv.org/abs/2610.16390 · HTML: https://mlsanity.com/paper/2610.16390/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 17 of 17)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep Learning and the Information Bottleneck Principle](https://mlsanity.com/paper/1503.02406/) (2015)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation](https://mlsanity.com/paper/2202.12233/) (2022)
- [RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing](https://mlsanity.com/paper/2111.04433/) (2021)
- [ASVspoof 5: Design, Collection and Validation of Resources for Spoofing, Deepfake, and Adversarial Attack Detection Using Crowdsourced Speech](https://mlsanity.com/paper/2502.08857/) (2025)
- [BUT Systems and Analyses for the ASVspoof 5 Challenge](https://mlsanity.com/paper/2408.11152/) (2024)
- [Learn from Real: Reality Defender's Submission to ASVspoof5 Challenge](https://mlsanity.com/paper/2410.07379/) (2024)
- [SZU-AFS Antispoofing System for the ASVspoof 5 Challenge](https://mlsanity.com/paper/2408.09933/) (2024)
- [CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification](https://mlsanity.com/paper/2409.15234/) (2024)
- [Reliable Audio Deepfake Detection in Variable Conditions via Quantum-Kernel SVMs](https://mlsanity.com/paper/2512.18797/) (2025)
- [Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning](https://mlsanity.com/paper/2507.14919/) (2025)
- [Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features](https://mlsanity.com/paper/2605.06035/) (2026)
- [SAFE Quantum Machine Learning with Variational Quantum Classifiers](https://mlsanity.com/paper/2605.16067/) (2026)
- [VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?](https://mlsanity.com/paper/2607.11985/) (2026)

## Abstract (arXiv)

A central goal of quantum machine learning is to identify applications in which small quantum circuits can improve task performance while remaining executable on real hardware. We investigate deepfake (synthetic) speech detection, a largely unexplored application in this area, where reliable confidence scores are as important as discrimination performance because deployment decisions are made at fixed thresholds and overconfident errors can be costly. We introduce QBFF, which routes a compressed speech representation through a small 8-qubit variational circuit (VQC) used as a drop-in bottleneck, with a matched classical bottleneck as the control. The quantum circuit yields better-calibrated confidence in most matched comparisons, including out-of-distribution evaluation, though the downstream deployment-cost benefit is model-dependent. We then run the same circuit on a real IBM quantum processor. To our knowledge, this is the first anti-spoofing pipeline to run on a QPU; hardware execution demonstrates feasibility, with lower subset EER and Cllr but higher mean actDCF than the matched classical reference. Together, these results show that hardware-executable quantum bottlenecks can improve score reliability, with gains that vary by evaluation setting.